Residue management and crop sequence effects on the yield and brown girdling root rot of canola
Bibliographic record
Abstract
Brown girdling root rot (BGRR) is a serious and widespread disease of canola (Brassica rapa L.) in the Peace River region of northwestern Canada. There is no chemical control treatment for the pathogen, and farmers have observed that the disease is more severe when canola follows red fescue (Festuca rubra L.) or clover (Trifolium spp.) compared to summer fallow. A field study was conducted to determine how crop sequences following red fescue termination can be combined with residue and tillage management to reduce BGRR infection and increase canola yield. The five treatments consisted of rotations of: continuous canola (CCC) and oat (Avena sativa L.)-oat-canola (OOC), both managed using reduced tillage (RT), and wheat (Triticum aestivum L.)-wheat-canola (WWC), managed using RT, conventional tillage (CT) or no-till (NT). Canola yield followed the trend: OOC (RT) = WWC(RT) > WWC(CT) > CCC(RT) = WWC(NT). BGRR infection increased with tillage intensity: WWC(CT) > CCC(RT) = WWC(RT) = OOC(RT) > WWC(NT), and was reduced when canola followed two cereal break crops. Yield was highest when canola was preceded by a cereal crop and lowest without a break crop. The low yield with NT was attributed to poor crop emergence from a hard seed bed with unbroken turf and to competition from re-emerged fescue in the third year after fescue breaking. This study demonstrated that the cropping sequence and tillage system used influenced canola yield to a greater extent than did BGRR infection. Key words: Brown girdling root rot, canola, crop sequence, red fescue termination, tillage
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".